Communication System Security

Coordinator(s)

Description

This study profile provides a solid background both for engineers involved in the design of secure systems and security officers in charge of corporate IT security. The main focus is the analysis of vulnerabilities and related solutions in the area of networking, computer systems and imaging. Various solutions ranging from cryptography and its applications to intrusion detection and practical countermeasures against network attacks through watermarking of images and biometric identification techniques are addressed in several courses.

MANDATORY COURSES

The goal of this course is to equip students with security and privacy technologies for the Big Data and the cloud computing paradigm. Students will discover the latest advances in privacy and security technologies and will understand their limitations as well.

This course provides a broad introduction to cryptography and communication security mechanisms based on cryptography. The course covers fundamental aspects such as security evaluation criteria and the mathematical constructs underlying cryptographic primitives as well as applied aspects like the design of major encryption and hashing algorithms, details of security mechanisms relying on cryptography such as data encryption, integrity, digital signature, authentication, key management, and public-key infrastructures.

This course provides an introduction to practical security concepts. The goal is to understand common attacks and countermeasures in a range of topics. The course is practice oriented, it describes real attacks and countermeasures. Students will practice attacks on a dedicated server (similar to a Capture the Flag competition).

Teaching and Learning Methods :Weekly class. Some guest lectures. Homework are online challenges, on a number of topics related to the class. A first lab is organized during lecture time to bootstrap challenges.

Course Policies :Class attendance is not checked but generally required to succeed.

Wireless communications are pervasive and have been used for a century. They are used in a very large set of security applications (communications by security forces, car key remote, alarm system, access control, drone command and control, surveillance devices) . However, day to day applications also require to be protected for privacy and personal security, such as WiFi or mobile communications (2G/3G/4G). At the same a number of challenges are present in wireless communications security, for example, messages are broadcasted, making it possible to intercept them without being noticed. Wireless signals are subject to jamming, making them unavailable.

This course will give a large perspective of the fundamental challenges in securing wireless communications, from the physical layer, modulations to the application protocols. A special focus will be put on practice with hands on exercises (using software defined radios and WiFi dongles).

Teaching and Learning Methods : Course is composed of lectures, Labs and small projects with final presentation.

The course is roughly divided in two separate parts. The first covers the topics of computer forensics and incident response. In particular, we discuss a number of techniques and open source tools to acquire and analyze network traces, hard disk images, Windows and Linux operating system artifacts, log files, and memory images.

The second part of the course deals with the analysis of malware and unknown binaries. Here the goal is to introduce students to the main classes of techniques used in malware analysis and reverse engineering. We cover both static techniques (ELF and PE file structures, dissasseblers and decompilers, data and control flow analysis, abstract interpretation, ...) and dynamic techniques (sandboxing, library and syscall traces, dynamic instrumentation, debugging, taint analysis, unpacking,...). We will use mostly open source tools, with the exception of IDA Pro.

This course offers a survey of several well-known attacks targeting specific weaknesses of hardware (microprocessors, dedicated hardware cryptographic accelerators...) For each of them the conditions of success are explained and some countermeasures are proposed.

Digital Watermarking allows owners or providers to hide an invisible and robust message inside a digital Multimedia document, mainly for security purposes such as owner or content authentication. There is a complex trade-off between the different parameters : capacity, visibility and robustness.

Steganographyis the art and science of writing hidden messages (in a picture or a video) in such a way that no-one apart from the sender and intended recipient even realizes there is a hidden message.

Image Forensics includes two main objectives: (1) To determine through which data acquisition device a given image is generated; (2) To determine whether a given image has undergone any form of modification or processing.

Biometrics: The security fields uses three different types of authentication : something you know, something you have, ore something you are : a biometric. Common physical biometrics includes fingerprints, hand geometry ; and retina, iris or facial characteristics. Behavioural characters include signature, voice. Ultimately, the technologies could find their strongest role as intertwined and complementary pieces of a multifactor authentication system. In the future biometrics is seen playing a key role in enhancing security, residing in smart cards and supporting personalized Web e-commerce services. Personalization through person authentication is also very appealing in the consumer product area. This course will focus on enabling technologies for Biometrics, with a particular emphasis on person verification and authentication based on or widely using image/video processing.

Video surveillance is the monitoring of the behavior, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting. By default, for a better scene understanding, automatic image processing tools are used between acquisition/transmission and visualization or storage

FREE COURSES

In this course, we will discuss contemporary and state of the art research problems in Data Science. The content of the course will change from year to year and will reflect the current research interests of the EURECOM faculty. The course is organised partly in Seminars/Case Studies sessions supported by industrials and researchers working in the field and a Mini Scientific Conference where each student will research and present a topic from the wide range of advanced data science topics.

Teaching and Learning Methods : academic and industrial seminars, case studies in small group, written and oral presentation.

The goal of this course is to provide a comprehensive view on recent topics and trends in distributed systems and cloud computing. We will discuss the software techniques employed to construct and program reliable, highly-scalable systems. We will also cover architecture design of modern datacenters and virtualization techniques that constitute a central topic of the cloud computing paradigm. The course is complemented by a number of lab sessions to get hands-on experience with Hadoop and the design of scalable algorithms with MapReduce.

This course presents the architecture of microprocessor-based systems, from the internals of the processors themselves to the main peripherals that surround them and make a complete computing machine, capable of running operating systems like GNU/Linux, Android, Windows, iOS...

This course covers the implementation of database systems by addressing the main topics, including data storage, indexing, querying; query optimization and execution; concurrency control and transaction management.

The purpose of the course is to become familiar with the principles and the ideas behind established techniques for handling data at scale. Students will implement classic and cutting-edge database systems methods in three projects. Projects represent the biggest chunk of this course. The projects require extending the functionality of a data management system in order to support novel features. In at least one the projects, students will also write a technical report that describes and experimentally evaluates the built system.

The course is complemented by lab sessions to guide students through the design and validation of the methods developed duringthe lectures.

Teaching and Learning Methods: Lectures and Lab sessions (preferably one student per group).

Course Policies :

Students are expected to do their own assigned work. If it is determined that a student has engaged in any form of academic dishonesty, he or she may fail the course and additional sanctions according to Eurecom's policies.

This course covers the fundamentals for the analysis and design of physical layer digital communication systems.

It serves as the basic building block for understanding modern mathematical procedures that enable communication via different physical media (e.g. radio, twisted-pair wireline, coaxial cable, fiber-optical).

Both the deterministic and random characterizations of common transmit signal and noise processes are covered as well as optimal receivers and their performance using different digital signalling methods.

Students considering this elective should note that the Grading Policy has been modified significantly from that used in previous years.

This course is designed to familiarize students with the practices and underlying issues surrounding the creation of new economic activity (with or without the founding of new firms) and with the funding of such initiatives. This involves learning about the key decisions to be made by the entrepreneur and about her/his relationship with potential financiers of new ventures.

Teaching and Learning Methods: Lectures and class discussions based on articles or book excerpts.

Course Policies: Attendance at all Lecture and Tutorial sessions is mandatory.

This course is an introduction to sustainable development and responsible innovation, particularly in the digital domain. It provides conceptual and empirical bases for students to approach technology and innovation in their interconnections with environmental, social and political factors. Students will learn the key concepts of the field (eg sustainability, acceptability, Triple Bottom line, life cycle approach, energy efficiency) and apply them to ICTs focusing on Green-IT. Topics covered in class include: smart grids & smart metering, big data analytics, energy consumption of digital activities, product eco-design, economy of functionality.

Teaching and Learning Methods: The course is organized as a work-shop where the sessions will be held alternately by social scientists and lecturers from outside the academic field (industry, consulting, public policies). It is based on a participative pedagogy, requiring the active involvement of students, both in class (discussions, MCQ, practical work) and at home (readings, research on the Web, writing). A personal written work of knowledge formalization will be required at the end of the course.

Because multimedia data (in particular image and video) require efficient compression techniques in order to be stored and delivered, image and video compression is a crucial element of an effective communication system.

This course covers the most popular lossless and lossy formats, introduces the key techniques used in source coding, as well as appropriate objective/subjective metrics for visual quality evaluation.

Teaching and Learning Methods: Each class includes a problem session for students to practice the material learned. This course includes a limited number of lab session hours.

The course aims at providing students with a basic knowledge and practice about the use of computer algorithms to perform image processing on digital images. The two main objectives attached to Digital Image Processing (DIP) are to improve the visual quality of images and to automatically extract semantic information from visual data (e.g. object recognition).

Teaching and Learning Methods: Each session is split into two parts: 1.5-hour lecture and 1.5-hour lab.

Since 1948, the year of publication of Shannon's landmark paper "A mathematical theory of communications", Information theory has paved the ground for the most important developments of today's information/communication world making it perhaps the most important theoretical tool to understand the fundamentals of information technologies.

Information theory studies the ultimate theoretical limits of source coding and data compression, of channel coding and reliable communications via channels, and provides the guidelines for the development of practical signal-processing and coding algorithms.

This course covers Information theory at an introductory level.

The practical implications of theoretical results presented are put in evidence through examples.

Various perspectives are given to understand every single theoretical results from a intuitive point of view, regardless of your background or study track.

This course is designed to explain what innovation is, what are the forms and types of innovation and how works the process issuing to generation of improved or new products. It explores the frameworks and the models implied from creativity and research stimulation to development, industrialization and access to the market.

Teaching and learning methods : Mix of lectures, group works and discussions.

The objective of this course is to give students a solid background in Machine Learning (ML) techniques. ML techniques are used to build efficient models for problems for which an optimal solution is unknown. This course will introduce the basic theories of Machine Learning, together with the most common families of classifiers and predictors. It will identify the basic ideas underlying the mechanism of learning, and will specify the practical problems that are encountered when applying these techniques, optimization, overfitting, validation, together with possible solutions to manage those difficulties.

This course is like a mini-MBA (Masters' in Business Administration) and covers much of the same ground as a business school classical post-experience MBA (though not in the same depth). This is one of a triad of related courses all of which are of special interest to those intending to become managers (practically everyone!), or, eventually, owners of their own companies.

Teaching and Learning Methods: Lectures, team exercises, and presentations

This course aims to present a treatment of mathematical methods suitable for engineering students who are interested in the rapidly advancing areas of signal analysis, processing, filtering and estimation. Significant current applications relate to, e.g., speech and audio, music, wired and wireless communications, instrumentation, multimedia, radar, sonar, control, biomedicine, transport and navigation. The course presents a study of linear algebra, probability, random variables, and analogue systems as a pre-requisite to material relating to sampled-data systems. Time permitting, the final part of the course covers the concepts of random processes, the analysis of random signals, correlation and spectral density.

Teaching and Learning Methods: The course is comprised of lectures, exercises and laboratory sessions.

Course policies: This course is aimed at students who have NOT already completed preparatory classes. Completion of all in-lecture examples is strongly advised.

The goal of MOBCOM is to provide a fundamental understanding of mobile communication systems. The course will seek to describe the key aspects of channel characteristics/modeling, of communication techniques, and to describe the application of these techniques in wireless communication systems.

This course will discuss all relevant aspects related to mobile systems security. Mobile devices have been revolutionized users' lives, and more than two billions mobile devices have been sold to date. Unfortunately, these devices, their operating systems, and the applications running on them are affected by security and privacy concerns. This course will be hands-on and will cover topics such as the mobile ecosystem, the design and architecture of mobile operating systems, rooting and jailbreaking, application analysis, malware reverse engineering, malware detection, vulnerability assessment, automatic static and dynamic analysis, and exploitation and mitigation techniques. While this course will mostly focus on Google's Android OS (its open nature makes it possible to have more interesting exercises and projects), it will also cover technical details about Apple's iOS as well.

This course presents the three main mobile platforms and their ecosystems, namely Android, iOS, and PhoneGap/WebOS. It explores emerging technologies and tools used to design and implement feature-rich mobile applications for smartphones and tablets taking into account both the technical constraints relative to storage capacity, processing capacity, display screen, communication interfaces, and the user interface, context and profile.

Teaching and Learning Methods : Lectures, Lab sessions (group of 2 students), and a challenge project ( group of up 2 4 students).

This course presents a series of mobile systems in their entirety to synthetize the knowledge gained in more fundamental courses. It explores current and emerging standards and follows the evolution of various mobile services.

The goal of this course is to teach student how to model, analyze, and optimize the performance of different Networks using simple theoretical tools. The end goal is to highlight the common underlying properties, develop a strong high-level insight on the network parameters affecting network performance, and understand how to optimize a networked system.

Each class will be a mix of some necessary theoretical tools, and their application to real-world networks. We will consider examples from modern cellular networks (e.g., offloading and load balancing), capacity planning, MAC protocols, scheduling in computing clouds and web server farms, security (e.g. virus infections), measuring large social networks like Facebook and Twitter, search engines (e.g. Google's PageRank algorithm), and many others.

Optimization is broadly applied to many technical and non-technical fields and provides a powerful set of tools for the design and analysis of communication systems and signal processing algorithms. This course addresses basic concepts of optimization and will introduce EURECOM students to fundamental concepts as duality and KKT conditions, widely utilized optimization techniques as linear and geometric programming and unconstrained optimization algorithms, but also to more advanced convex optimization techniques, which have been widely applied in wireless communications nowadays, such as second order cone programming and semidefinite programming.

Special emphasis is devoted to exemplify applications of optimization techniques to telecommunications problems with the objective of developing skills and background necessary to recognize, formulate, and solve optimization problems.

Would you like to investigate beyond the surface of Windows, MacOS, Linux, Android? Fed up with not understanding the origin of segmentation faults, why you need to eject a USB key before physically removing it, or why/how your Android system can execute Pokemon Go and Facebook at the same time? You want to delve into the details of the inner workings of the Linux kernel? Join us to discover the power of Operating Systems!

This course covers a variety of topics, all related to the use and management of a Linux operating system. In particular, the course is divided in three parts dedicated respectively to the command-line, to the Python programming language, and to maintaining, compiling, and installing applications.

The proper treatment of modern communication systems requires the modelling of signals as random processes. Often the signal description will involve a number of parameters such as carrier frequency, timing, channel impulse response, noise variance, interference spectrum. The values of these parameters are unknown and need to be estimated for the receiver to be able to proceed.

Parameters may also occur in the description of other random analysis of communication networks, or in the descriptions of sounds and images, or other data, e.g. geolocation. This course provides an introduction to the basic techniques for estimation of a finite set of parameters, of a signal spectrum or of one complete signal on the basis of a correlated signal (optimal filtering, Wiener and Kalman filtering). The techniques introduced in this course have a proven track record of many decades. They are complementary to the techniques introduced in the EURECOM course Stat. They are useful for other application branches such as machine learning, in the EURECOM courses MALIS and ASI.

Teaching and Learning Methods: Lectures, Homework, Exercise and Lab session (groups of 1-2 students depending on size of class).

Course Policies: Attendance of Lab session is mandatory (15% of final grade).

This course is an introduction to statistics. The goal is to equip students with fundamentals in statistics in order to apply this knowledge in solving practical engineering problems. The students will be taught different statistical methods and should be able to make meaningful inferences on relevant datasets.

Teaching and Learning Methods: The course is comprised of lectures, exercises and laboratory sessions.

Course Policies: Attendance to exercises and lab sessions is mandatory.

(1) 'Know yourself' - understanding the drivers of your own behavior. This is the basis of any personal development and is critical for developing effective interaction with others whether as a team member, or as a team leader.

(2) 'Working with others' - building on the self-knowledge mentioned above, this core element allows you to explore, understand, and practice ways of working with others that are both more enjoyable and more effective. This is critical given that almost everyone works as part of a team.

(3) 'What's next?' - building on both the above sections, this element helps you take the next steps in your career: setting objectives, selecting target organizations, applying for jobs, and effective interviewing.

Teaching and Learning Methods: Lectures, team exercises, and presentations

« Those who fail to plan, plan to fail... ». Architects, tailors, and directors all use plans (or models) for their creation, and software engineers are no exception. Thus, it is a common practice for software project managers to rely on the UML langage to document their software projects, and to perform modeling of the software itself.

Human-computer interaction (HCI) is the study of interaction between people (users) and computers, as the intersection of computer science, behavioral sciences, design and several other fields of study. This course aims to provide the basic concepts of user centered design when developing web applications. It will offer a deep dive presentation of modern web technologies: HTML5, CSS3 and Javascript. Finally, this course will provide techniques for evaluating user interfaces.

This course introduces the main concepts and techniques used in computer graphics and image synthesis. It focuses on 3D object modelling and advanced visualization methods used in 3D and Virtual imaging, scientific and information visualization, CAD, flight simulation, games, advertising and movie special effects. The courses mixes theoretical and practical sessions and the project requires a student personal involvement.

This course aims at providing a solid and practical algorithmic foundation to the design and use of scalable machine learning algorithms, with particular emphasis on the MapReduce programming model. Students will get familiar with a wide range of topics, through the application of theoretic ideas on problems of practical interest. This is a "reverse class", in which students are required to study (or revise) a particular topic at home, and apply what they have learned solving real world problems, including industrial applications, during numerous laboratory sessions. Laboratory sessions are based on modern technologies such as Jupyter Notebooks.

This course covers the application-level protocols dedicated to IOT. Knowing the limited capacity, in terms of battery and CPU, of the things, the classical application protocols used in the Internet like HTTP are not adequate. This course presents the recent application protocols specially developed for IOT. These protocols are organized into two categories: (i) Client/server (like COAP) and (ii) Publish/Subscribe (like MQTT, XMPP). In addition to these protocols, this course introduces two types of architecture, specifically dedicated to host IOT services, like 3GPP MTC and oneM2M.

Teaching and Learning Methods: The course is organised in lectures and labs.

This course focuses on the principles of learning from data and quantification of uncertainty, by complementing and enriching the Introduction to Statistical Learning course. In particular, the course is divided into two main parts that correspond to the supervised and unsupervised learning paradigms. The presentation of the material follows a common thread based on the probabilistic data modeling approach, so that many classical algorithms, such as least squares and k-means, can be seen as special cases of inference problems for more general probabilistic models. Taking a probabilistic view also allows the course to derive inference algorithms for a class of nonparametric models that have close connections with neural networks and support vector machines. Similarly to the Introduction to Statistical Learning course, the focus is not on the algorithmic background of the methods, but rather on their mathematical and statistical foundations. This advanced course is complemented by lab sessions to guide students through the design and validation of the methods developed duringthe lectures.

In the Business Simulation course, students, in groups of four to six, will manage a virtual company as an aid to learning, by doing, about the practical aspects of running a company in a dynamic international environment. The course will be provided in a compact blended learning environment.

Uniquely at Eurecom, this course will be delivered in a blended learning environment. That is, only half of the learning will take place in the classroom at fixed times each week. The other half of the course will be undertaken online at times, and places, suitable for the individual student teams, provided that the required tasks (usually a decision set) is completed within the defined week timeframe.

Research has shown that the best learning experience from the business simulation is over a concentrated timeframe. Therefore, this course, of the standard 42 hours effective learning time for a 5-credit program, will be completed over seven weeks elapsed time (rather than the standard 14 weeks). Some students may find this helpful; freeing up time towards the end of the semester to work on projects in other courses.

During the course, following initial briefings, student teams will each take up to 12 sets of business decisions; each decision set representing one quarter of a business year. Decisions are entered online before a predefined cutoff date and time. These decision sets drive the simulation, the results being provided online. During the seven classroom sessions, instructors will be available, face-to-face, to answer questions and provide support. Between the classroom sessions, instructors are available online (asynchronously, and, at pre-agreed times, live), as are a range of online support materials, including videos and guides.

Teaching will combine classroom and video-based instruction, guidance, and support, with additional online materials and individual support helping students, at their own time and pace, to master the technical and practical aspects of the simulation.

Course Policies:

Active participation is required from each student. The grading system is continuous (see below) and is on both team and individual results. On-time attendance at entire classroom sessions is mandatory and will be recorded. Unapproved absences may result in expulsion. Individual student participation during the online sessions will be monitored by the teams themselves. Peer reviews are part of the evaluation process. In the final classroom session, each team, involving each individual student, will present their results to the class and to assessors.

Coding is a crucial aspect of communications, as it allows for meeting the fundamental limits in a reliable manner. In the center of coding lie a variety of techniques for designing error detecting and correcting codes, which are the corner stone of any communications systems. Coding theory also gives insights as to how to design modern network systems: such techniques include network coding, index coding, memory-aided coding, etc. This short course will seek to expose the student to a variety of theoretical and practical aspects of modern error correcting codes as well as to other coding theoretic aspects. In addition to theory, the course will also provide practical training in the form of MATLAB sessions, as well as homework assignments.

Deep Learning is a new approach in Machine Learning which allows to build models that have shown superior performance fora wide range of applications, in particular Computer Vision and Natural Language Processing. Thanks to the joint availability of large data corpus and affordable processing power, Deep Learning has revived the old field of Artificial Neural Networks and provoked the "Renaissance" of AI (Artificial Intelligence). The objective of this course is to provide an overview of the field of Deep Learning, starting from simple Neural Network architectures and pursuing with contemporary and state of the art practices and models. The course is organized as a combination of lectures where the theory is exposed and discussed, and hands-on sessions (labs) where experimentations are performed to practice with the theoretical concepts.

Teaching and Learning Methods : The course is composed of a combination of lectures and labs.

This course provides an overview of software and hardware design for smart objects. It shows how to specify, design and validate digital hardware components, how to integrate them in a microprocessor-based system, and how to drive them from the software layers.

Teaching and Learning Methods: Lectures, team-work, lab sessions. Students are provided with prototyping boards and design tools for the whole semester duration.

The aims of the course are to provide students with tools that can help to design error-free software/hardware systems. The course gives both the theoretical foundations and the pratical use of formal methods.

This course provides a solid introduction to European Business law from a managerial and strategic perspective taking an international and comparative approach.The course is the study of how companies manage legal perspectives in order to create value for the company through corporate and contract issues.The course will focus on the main laws that regulate various aspects of establishing and running a business within the European Union.

This module addresses the access methods in Wireless Local Access Networks (WLAN). The basic contention and management mechanisms are detailed. Current and emerging standards of WLAN toward 5G are also presented.

The architectures of networks and service delivery platforms are subject to an unprecedented techno-economic transformation. This trend, often referred to as Network Softwarization, will yield significant benefits in terms of reducing expenditure and operational costs of next generation networks. The key enablers are Network Function Virtualization (NFV), Software-Defined Networking (SDN), Cloud Computing (mainly Edge Computing).

This course will cover the principle of Network Softwerization by introducing and detailing the concepts of SDN, NFV and Cloud Computing (focusing on the IaaS model and Edge Computing). Besides covering the theoretical aspects, the course will provide an overview of the enabling technologies, and how combining these concepts will allow building flexible and dynamic virtual networks tailored to services, e.g. Anything as a Service (AaaS) and Network Slicing.

The project oriented approach is considered in leading companies as an efficient method to manage both market and client oriented deliverables (i.e. products and services) as well as investments. In order to better manage and control projects, enterprises often evolve from a “Functional organisation” into a “Matrix organisation”, in which a new breed of leaders appears: Project Managers. The Project Management Profession becomes a key element in the new and global enterprise model.
The EURECOM Global Project Management class aims at introducing the different Project Management concepts and techniques, mixing “main tent” presentation of key topics and hands-on case studies for each student to experience team dynamics and managing sample projects.

Teaching and Learning Methods include Lectures (all attendees) and Case Study sessions (in groups).
A case study in the technology domain will be performed during the course, from session to session. The main purpose of this case study is to illustrate, use and get familiar with the different Project Management methods and techniques introduced during the lectures. The case study may require some work between the class sessions. Students will present their work to the whole class for the purpose of sharing and obtaining feed-back. In order to optimize the effectiveness of each session, the students will be expected to keep the topics addressed in earlier sessions fresh in their mind, prepare for the case study, and actively participate through questions and presentation of the results of the case study.

Course Policies: Attendance is Mandatory for Both Lectures and Case Study sessions.Non-attendance would need to be justified by serious reasons and limited to a maximum of 2.
Active participation in the Case Study sessions is expected.

This course treats the subject of modern radio engineering and includes typical RF architectures and their characterizations, modeling, prediction and simulation of radio-wave propagation, cellular planning, systems-level aspects of modern radio network design.

Contemporary works in the sociology of Technology offer numerous critics of the classical divide between technical and social features. It has been shown that the success or failure of technical innovations rests on their propensity to merge with various organizational and interactional features. This course aims at providing students with a precise understanding of different combinations between technologies and conversational features. Various case studies of technologies in use will be examined, either in professional or ordinary or in mundane contexts. Drawing from those studies, the course provide several methodological discussions, with a strong focus on observation of social conduct in natural settings and the use of audio or video recordings in social science.

This course presents the main applications of secure communication mechanisms in the area of computer networks and distributed systems. The course covers network security approaches based on firewalls, cryptographic security protocol suites designed for the data exchange and network control components of Internet, wireless security protocols, and security solutions for mobile network architectures.

The subtitle of this course could be “Multi-Antenna Interference Handling for Multi-User Multi-Cell Systems”. Indeed the main focus is on the exploitation of multiple antennas to (more easily) handle inter-symbol and inter-user interference. Key concepts here are beamforming, MIMO (Multi-Input Multi-Output), Multi-User MIMO, Massive MIMO.

After a basic course in digital communications, a wide range of issues arise in the treatment of physical layer procedures in a wide variety of transmission technologies such as xDSL, gigabit Ethernet, powerline systems, DAB/DVB broadcasting and optical communication systems to name a few. These issues involve e.g. multi-rate echo cancellation for full duplex operation on twisted pair telephone lines, synchronization and equalization techniques in a variety of single and multi-carrier systems, impulsive noise in powerline and automotive systems etc. Even just wireless communications encompass a wide range of systems such as satellite, underwater, near-field communications, fixed wireless access, private systems, sensors, IoT, etc. and a wide range of aspects such as relaying, full duplex radio, cognitive radio, location estimation etc.

Whereas these systems will be briefly mentioned, the main focus will be on cellular wireless and the use of multiple antennas at receivers and transmitters. Spatial filtering, spatiotemporal filtering, and multiuser detection for CDMA are all treated in a unified fashion.

Teaching and Learning Methods: Lectures, Exercise and Lab session (groups of 1-2 students depending on size of class).

Course Policies: Attendance of Lab session is mandatory (25% of final grade).

This course provides an introduction to the automatic processing of speech and audio signals. It starts with a treatment of the human speech production and perception mechanisms and looks at how our understanding of them has influenced attempts to process speech and audio signals automatically. The course then considers the analysis, coding and parameterisation of signals in the case of different speech and audio processing tasks. After an introduction to essential pattern recognition techniques, the course considers specific applications including speech recognition, speaker recognition and speaker diarization. The course also includes a treatment of speech and audio coding, noise compensation and speech enhancement.

Teaching and Learning Methods:

The course is comprised of lectures and exercises and laboratory sessions.

The Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined. It derives from W3C director Sir Tim Berners-Lee's vision of the Web as a universal medium for data, information, and knowledge exchange. This course is a guided tour for a number of W3C recommendations allowing to represent (RDF/S, SKOS, OWL) and query (SPARQL) knowledge on the web as well as the underlying logical formalisms of these languages, their syntax and semantics. We will present the problems of modeling ontologies and reconciling data on the web. Finally, we will explain how to extract knowledge from textual documents using natural language processing and information extraction technologies.

Teaching and Learning Methods:Lectures and Lab sessions (group of 2 students max)